Hands On Projects and Problem Solving Questions

Discussion of practical projects and side work you have built or contributed to across domains. Candidates should be prepared to explain their role, architecture and design decisions, services and libraries chosen, alternatives considered, trade offs made, challenges encountered, debugging and troubleshooting approaches, performance optimization, testing strategies, and lessons learned. This includes independent side projects, security labs and capture the flag practice, bug bounty work, coursework projects, and other hands on exercises. Interviewers may probe for how you identified requirements, prioritized tasks, collaborated with others, measured impact, and what you would do differently in hindsight.

EasyTechnical

0 practiced

Write a Dockerfile for a simple Python Flask web application that follows best practices: minimal image size, non-root user, pinned dependencies, an efficient layer ordering, and an optional multi-stage build. Explain your choices for base image, caching, and security hardening.

EasyBehavioral

0 practiced

Describe a time you triaged and fixed a production bug. Explain how you detected the issue, reproducible steps, debugging tools used (logs, tracing, profilers), the immediate mitigation you applied, how you rolled forward the fix, and what you documented in the postmortem.

MediumTechnical

0 practiced

In projects deployed to Kubernetes, explain how you set resource requests and limits, design liveness and readiness probes, configure horizontal or vertical autoscaling (HPA/VPA), and ensure rolling updates without downtime for both stateless and stateful workloads.

MediumTechnical

0 practiced

You discover a security vulnerability in a commonly used third-party library. Describe your remediation workflow: triage and impact assessment, CVE tracking, temporary mitigations, upgrade/backport decisions, testing plan across dependent services, deployment sequencing, and stakeholder communication.

HardTechnical

0 practiced

A distributed API aggregates responses from multiple microservices and suffers high tail latency (p99). Propose architectural and runtime strategies to reduce tail latency: hedging/speculative retries, parallelization, timeouts and fallbacks, request prioritization and batching, and approaches to measure and validate tail improvements.

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